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The impact of social housing on mental health: longitudinal analyses using marginal structural models and machine learning-generated weights
International Journal of Epidemiology ( IF 6.4 ) Pub Date : 2018-06-26 , DOI: 10.1093/ije/dyy116
Rebecca Bentley 1, 2 , Emma Baker 2 , Koen Simons 3 , Julie A Simpson 3 , Tony Blakely 3, 4
Affiliation  

Background
Social housing may provide an affordable and secure residential environment, but has also been associated with stigma, poor housing conditions and locational disadvantage. We examined the cumulative effect of additional years, and tenure security (number of transitions in/out), of social housing on mental health in a large cohort of lower-income Australians.
Methods
We analysed a longitudinal panel survey that annually collected information on tenure and health from 2001 to 2013. To address the time-varying effect of previous health on social housing occupancy, we used marginal structural models. Stabilized inverse probabilities of treatment weights were generated using ensemble learning to improve prediction. To address remaining residual imbalance across covariates, double adjustment was made by additionally including baseline covariates in models. Mental health was measured using the Mental Health Short-Form summary measure of the SF-36 (MH), and psychological distress was measured using the Kessler Psychological Distress Scale (K10).
Results
People who had continuous exposure to social housing had worse mental health on average than people continuously occupying other tenures. The worst mental health outcomes, however, were observed for people who made multiple transitions. Mental health deteriorated and psychological distress increased with number of transitions: MH −1.04 [95% confidence interval (CI) −2.16; 0.09) and K10 0.56 (95% CI 0.12; 1.00). Estimates are in the order of 6% (MH) and 9% (K10) of one standard deviation for each measure.
Conclusions
The more transitions people made in/out of social housing, the greater the impact on mental health and psychological distress, supporting the case for provision of more stable forms of social housing.


中文翻译:

社会住房对心理健康的影响:使用边际结构模型和机器学习生成的权重进行纵向分析

背景
社会住房可以提供负担得起且安全的居住环境,但也与污名,恶劣的住房条件和区位劣势相关。我们检查了一大批低收入澳大利亚人的社会住房对社会健康的额外年限和保有权保障(进出过渡次数)的累积影响。
方法
我们分析了一项纵向面板调查,该调查从2001年到2013年每年收集有关权属和健康的信息。为了解决以前的健康对社会住房入住率的时变影响,我们使用了边际结构模型。使用集成学习可提高治疗权重的稳定反概率。为了解决协变量之间剩余的剩余不平衡问题,通过在模型中另外包括基线协变量来进行双重调整。使用SF-36(MH)的“心理健康简短表”摘要测量精神健康,并使用Kessler心理困扰量表(K10)测量心理困扰。
结果
持续接触社会住房的人的心理健康状况平均要比持续占据其他任期的人的心理健康状况更差。但是,对于进行了多次过渡的人们,他们的心理健康结果最差。心理健康恶化和心理困扰随着过渡次数的增加而增加:MH -1.04 [95%置信区间(CI)-2.16;0.09)和K10 0.56(95%CI 0.12; 1.00)。每种度量的估计值约为一个标准偏差的6%(MH)和9%(K10)。
结论
人们进出社会住房的过渡越多,对心理健康和心理困扰的影响就越大,这为提供更稳定形式的社会住房提供了支持。
更新日期:2018-11-02
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